Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 KiB
Average record size in memory68.3 B

Variable types

Numeric3
Text3
Categorical2

Alerts

shp_cd is highly overall correlated with shp_ctprvn_addr and 1 other fieldsHigh correlation
shp_la is highly overall correlated with shp_ctprvn_addr and 1 other fieldsHigh correlation
shp_lo is highly overall correlated with shp_ctprvn_addr and 1 other fieldsHigh correlation
shp_ctprvn_addr is highly overall correlated with shp_cd and 3 other fieldsHigh correlation
shp_signgu_addr is highly overall correlated with shp_cd and 3 other fieldsHigh correlation
shp_cd has unique valuesUnique
shp_la has unique valuesUnique
shp_lo has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:41:32.425432
Analysis finished2023-12-10 09:41:36.077193
Duration3.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

shp_cd
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11539.99
Minimum10009
Maximum41525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:41:36.247265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10009
5-th percentile10046.9
Q110338.5
median10685.5
Q310966.25
95-th percentile11116.4
Maximum41525
Range31516
Interquartile range (IQR)627.75

Descriptive statistics

Standard deviation5310.0437
Coefficient of variation (CV)0.46014283
Kurtosis29.640962
Mean11539.99
Median Absolute Deviation (MAD)310
Skewness5.5597122
Sum1153999
Variance28196564
MonotonicityNot monotonic
2023-12-10T18:41:36.546994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10009 1
 
1.0%
10774 1
 
1.0%
10861 1
 
1.0%
10849 1
 
1.0%
10847 1
 
1.0%
10807 1
 
1.0%
10797 1
 
1.0%
10794 1
 
1.0%
10792 1
 
1.0%
10787 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
10009 1
1.0%
10033 1
1.0%
10035 1
1.0%
10039 1
1.0%
10045 1
1.0%
10047 1
1.0%
10076 1
1.0%
10081 1
1.0%
10082 1
1.0%
10087 1
1.0%
ValueCountFrequency (%)
41525 1
1.0%
41524 1
1.0%
41523 1
1.0%
11132 1
1.0%
11124 1
1.0%
11116 1
1.0%
11107 1
1.0%
11092 1
1.0%
11087 1
1.0%
11086 1
1.0%

shp_nm
Text

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:41:37.037021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.1
Min length2

Characters and Unicode

Total characters510
Distinct characters182
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)94.0%

Sample

1st row씨티마트
2nd row권선베스토아
3rd row태광통신
4th row인터뱅세계주류
5th row베스토아
ValueCountFrequency (%)
복권나라 4
 
3.7%
씨스페이스 2
 
1.9%
행운의집 2
 
1.9%
대한빌딩 1
 
0.9%
에덴 1
 
0.9%
씨티마트 1
 
0.9%
ok마트 1
 
0.9%
갤러리마트 1
 
0.9%
대박.com 1
 
0.9%
응암역점 1
 
0.9%
Other values (92) 92
86.0%
2023-12-10T18:41:37.816154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 30
 
5.9%
권 19
 
3.7%
복 18
 
3.5%
마 17
 
3.3%
트 15
 
2.9%
스 13
 
2.5%
방 12
 
2.4%
수 10
 
2.0%
퍼 10
 
2.0%
대 10
 
2.0%
Other values (172) 356
69.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 459
90.0%
Lowercase Letter 13
 
2.5%
Decimal Number 10
 
2.0%
Uppercase Letter 9
 
1.8%
Space Separator 7
 
1.4%
Open Punctuation 5
 
1.0%
Close Punctuation 5
 
1.0%
Other Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 30
 
6.5%
권 19
 
4.1%
복 18
 
3.9%
마 17
 
3.7%
트 15
 
3.3%
스 13
 
2.8%
방 12
 
2.6%
수 10
 
2.2%
퍼 10
 
2.2%
대 10
 
2.2%
Other values (146) 305
66.4%
Lowercase Letter
ValueCountFrequency (%)
a 3
23.1%
m 2
15.4%
t 1
 
7.7%
r 1
 
7.7%
o 1
 
7.7%
c 1
 
7.7%
s 1
 
7.7%
y 1
 
7.7%
w 1
 
7.7%
l 1
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
S 3
33.3%
K 2
22.2%
U 1
 
11.1%
N 1
 
11.1%
O 1
 
11.1%
G 1
 
11.1%
Decimal Number
ValueCountFrequency (%)
2 6
60.0%
1 1
 
10.0%
4 1
 
10.0%
5 1
 
10.0%
3 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 459
90.0%
Common 29
 
5.7%
Latin 22
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 30
 
6.5%
권 19
 
4.1%
복 18
 
3.9%
마 17
 
3.7%
트 15
 
3.3%
스 13
 
2.8%
방 12
 
2.6%
수 10
 
2.2%
퍼 10
 
2.2%
대 10
 
2.2%
Other values (146) 305
66.4%
Latin
ValueCountFrequency (%)
a 3
13.6%
S 3
13.6%
K 2
 
9.1%
m 2
 
9.1%
U 1
 
4.5%
N 1
 
4.5%
t 1
 
4.5%
O 1
 
4.5%
r 1
 
4.5%
o 1
 
4.5%
Other values (6) 6
27.3%
Common
ValueCountFrequency (%)
7
24.1%
2 6
20.7%
( 5
17.2%
) 5
17.2%
, 1
 
3.4%
1 1
 
3.4%
. 1
 
3.4%
4 1
 
3.4%
5 1
 
3.4%
3 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 459
90.0%
ASCII 51
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 30
 
6.5%
권 19
 
4.1%
복 18
 
3.9%
마 17
 
3.7%
트 15
 
3.3%
스 13
 
2.8%
방 12
 
2.6%
수 10
 
2.2%
퍼 10
 
2.2%
대 10
 
2.2%
Other values (146) 305
66.4%
ASCII
ValueCountFrequency (%)
7
13.7%
2 6
 
11.8%
( 5
 
9.8%
) 5
 
9.8%
a 3
 
5.9%
S 3
 
5.9%
K 2
 
3.9%
m 2
 
3.9%
, 1
 
2.0%
1 1
 
2.0%
Other values (16) 16
31.4%

shp_ctprvn_addr
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
51 
경기
18 
인천
8 
강원
7 
대전
6 
Other values (4)
10 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row서울
2nd row경기
3rd row서울
4th row서울
5th row서울

Common Values

ValueCountFrequency (%)
서울 51
51.0%
경기 18
 
18.0%
인천 8
 
8.0%
강원 7
 
7.0%
대전 6
 
6.0%
충남 4
 
4.0%
충북 4
 
4.0%
울산 1
 
1.0%
전남 1
 
1.0%

Length

2023-12-10T18:41:38.064510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:38.275814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 51
51.0%
경기 18
 
18.0%
인천 8
 
8.0%
강원 7
 
7.0%
대전 6
 
6.0%
충남 4
 
4.0%
충북 4
 
4.0%
울산 1
 
1.0%
전남 1
 
1.0%

shp_signgu_addr
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
중구
8 
강남구
 
7
수원시
 
6
종로구
 
5
송파구
 
4
Other values (42)
70 

Length

Max length4
Median length3
Mean length2.93
Min length2

Unique

Unique20 ?
Unique (%)20.0%

Sample

1st row강남구
2nd row수원시
3rd row성북구
4th row강남구
5th row강남구

Common Values

ValueCountFrequency (%)
중구 8
 
8.0%
강남구 7
 
7.0%
수원시 6
 
6.0%
종로구 5
 
5.0%
송파구 4
 
4.0%
원주시 4
 
4.0%
서구 3
 
3.0%
청주시 3
 
3.0%
서산시 3
 
3.0%
서초구 3
 
3.0%
Other values (37) 54
54.0%

Length

2023-12-10T18:41:38.516048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중구 8
 
8.0%
강남구 7
 
7.0%
수원시 6
 
6.0%
종로구 5
 
5.0%
송파구 4
 
4.0%
원주시 4
 
4.0%
서산시 3
 
3.0%
서초구 3
 
3.0%
청주시 3
 
3.0%
서구 3
 
3.0%
Other values (37) 54
54.0%

shp_emd_addr
Text

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:41:39.371755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length4.83
Min length2

Characters and Unicode

Total characters483
Distinct characters132
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)93.0%

Sample

1st row도산대로 49길
2nd row권선구 세권로181번길
3rd row동소문로
4th row언주로
5th row역삼로
ValueCountFrequency (%)
덕릉로 3
 
2.7%
팔달구 3
 
2.7%
동소문로 2
 
1.8%
중앙로 2
 
1.8%
청원구 2
 
1.8%
남원로 1
 
0.9%
도산대로 1
 
0.9%
한천로 1
 
0.9%
평택로76번길 1
 
0.9%
중앙2로 1
 
0.9%
Other values (95) 95
84.8%
2023-12-10T18:41:40.125664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 98
 
20.3%
길 30
 
6.2%
1 15
 
3.1%
번 13
 
2.7%
구 12
 
2.5%
12
 
2.5%
동 11
 
2.3%
대 11
 
2.3%
4 8
 
1.7%
산 8
 
1.7%
Other values (122) 265
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 408
84.5%
Decimal Number 63
 
13.0%
Space Separator 12
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 98
24.0%
길 30
 
7.4%
번 13
 
3.2%
구 12
 
2.9%
동 11
 
2.7%
대 11
 
2.7%
산 8
 
2.0%
원 8
 
2.0%
문 6
 
1.5%
중 6
 
1.5%
Other values (111) 205
50.2%
Decimal Number
ValueCountFrequency (%)
1 15
23.8%
4 8
12.7%
3 7
11.1%
6 6
 
9.5%
2 6
 
9.5%
0 5
 
7.9%
9 5
 
7.9%
7 5
 
7.9%
5 4
 
6.3%
8 2
 
3.2%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 408
84.5%
Common 75
 
15.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 98
24.0%
길 30
 
7.4%
번 13
 
3.2%
구 12
 
2.9%
동 11
 
2.7%
대 11
 
2.7%
산 8
 
2.0%
원 8
 
2.0%
문 6
 
1.5%
중 6
 
1.5%
Other values (111) 205
50.2%
Common
ValueCountFrequency (%)
1 15
20.0%
12
16.0%
4 8
10.7%
3 7
9.3%
6 6
 
8.0%
2 6
 
8.0%
0 5
 
6.7%
9 5
 
6.7%
7 5
 
6.7%
5 4
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 408
84.5%
ASCII 75
 
15.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 98
24.0%
길 30
 
7.4%
번 13
 
3.2%
구 12
 
2.9%
동 11
 
2.7%
대 11
 
2.7%
산 8
 
2.0%
원 8
 
2.0%
문 6
 
1.5%
중 6
 
1.5%
Other values (111) 205
50.2%
ASCII
ValueCountFrequency (%)
1 15
20.0%
12
16.0%
4 8
10.7%
3 7
9.3%
6 6
 
8.0%
2 6
 
8.0%
0 5
 
6.7%
9 5
 
6.7%
7 5
 
6.7%
5 4
 
5.3%

shp_detail_addr
Text

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:41:40.701652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length5.34
Min length1

Characters and Unicode

Total characters534
Distinct characters81
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)83.0%

Sample

1st row8
2nd row20-23
3rd row248, 105동 제1층 1호(길음동, 삼부아파트)
4th row647
5th row166
ValueCountFrequency (%)
8 4
 
3.1%
1 3
 
2.3%
1층 3
 
2.3%
2 3
 
2.3%
3 2
 
1.5%
5 2
 
1.5%
14 2
 
1.5%
46 2
 
1.5%
104호 2
 
1.5%
6 2
 
1.5%
Other values (104) 106
80.9%
2023-12-10T18:41:41.449588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 83
15.5%
2 42
 
7.9%
31
 
5.8%
0 30
 
5.6%
3 27
 
5.1%
4 26
 
4.9%
5 25
 
4.7%
8 24
 
4.5%
6 22
 
4.1%
, 18
 
3.4%
Other values (71) 206
38.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 314
58.8%
Other Letter 123
 
23.0%
Space Separator 31
 
5.8%
Other Punctuation 21
 
3.9%
Dash Punctuation 17
 
3.2%
Open Punctuation 13
 
2.4%
Close Punctuation 13
 
2.4%
Uppercase Letter 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 13
 
10.6%
호 10
 
8.1%
층 9
 
7.3%
가 7
 
5.7%
딩 6
 
4.9%
빌 6
 
4.9%
정 3
 
2.4%
로 3
 
2.4%
대 3
 
2.4%
신 2
 
1.6%
Other values (52) 61
49.6%
Decimal Number
ValueCountFrequency (%)
1 83
26.4%
2 42
13.4%
0 30
 
9.6%
3 27
 
8.6%
4 26
 
8.3%
5 25
 
8.0%
8 24
 
7.6%
6 22
 
7.0%
9 18
 
5.7%
7 17
 
5.4%
Other Punctuation
ValueCountFrequency (%)
, 18
85.7%
. 2
 
9.5%
@ 1
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
W 1
50.0%
Space Separator
ValueCountFrequency (%)
31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 409
76.6%
Hangul 123
 
23.0%
Latin 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 13
 
10.6%
호 10
 
8.1%
층 9
 
7.3%
가 7
 
5.7%
딩 6
 
4.9%
빌 6
 
4.9%
정 3
 
2.4%
로 3
 
2.4%
대 3
 
2.4%
신 2
 
1.6%
Other values (52) 61
49.6%
Common
ValueCountFrequency (%)
1 83
20.3%
2 42
10.3%
31
 
7.6%
0 30
 
7.3%
3 27
 
6.6%
4 26
 
6.4%
5 25
 
6.1%
8 24
 
5.9%
6 22
 
5.4%
, 18
 
4.4%
Other values (7) 81
19.8%
Latin
ValueCountFrequency (%)
S 1
50.0%
W 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 411
77.0%
Hangul 123
 
23.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 83
20.2%
2 42
10.2%
31
 
7.5%
0 30
 
7.3%
3 27
 
6.6%
4 26
 
6.3%
5 25
 
6.1%
8 24
 
5.8%
6 22
 
5.4%
, 18
 
4.4%
Other values (9) 83
20.2%
Hangul
ValueCountFrequency (%)
동 13
 
10.6%
호 10
 
8.1%
층 9
 
7.3%
가 7
 
5.7%
딩 6
 
4.9%
빌 6
 
4.9%
정 3
 
2.4%
로 3
 
2.4%
대 3
 
2.4%
신 2
 
1.6%
Other values (52) 61
49.6%

shp_la
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.339518
Minimum34.794026
Maximum37.90222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:41:41.700765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.794026
5-th percentile36.351014
Q137.333164
median37.507325
Q337.56774
95-th percentile37.747771
Maximum37.90222
Range3.1081933
Interquartile range (IQR)0.23457637

Descriptive statistics

Standard deviation0.47774892
Coefficient of variation (CV)0.012794727
Kurtosis8.9607518
Mean37.339518
Median Absolute Deviation (MAD)0.0791411
Skewness-2.6313532
Sum3733.9518
Variance0.22824403
MonotonicityNot monotonic
2023-12-10T18:41:41.948419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5233512 1
 
1.0%
37.6340109 1
 
1.0%
36.9938104 1
 
1.0%
36.9919884 1
 
1.0%
37.5528913 1
 
1.0%
37.4969454 1
 
1.0%
37.5809287 1
 
1.0%
37.6114788 1
 
1.0%
37.6001182 1
 
1.0%
37.6455202 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
34.7940263 1
1.0%
35.5969505 1
1.0%
36.31117 1
1.0%
36.3247933 1
1.0%
36.3503767 1
1.0%
36.3510472 1
1.0%
36.3565217 1
1.0%
36.4476698 1
1.0%
36.6465287 1
1.0%
36.6577103 1
1.0%
ValueCountFrequency (%)
37.9022196 1
1.0%
37.8989474 1
1.0%
37.8806202 1
1.0%
37.8603675 1
1.0%
37.7512587 1
1.0%
37.7475877 1
1.0%
37.6630817 1
1.0%
37.6615855 1
1.0%
37.6455202 1
1.0%
37.6394732 1
1.0%

shp_lo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.10707
Minimum126.38013
Maximum129.36685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:41:42.214183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.38013
5-th percentile126.598
Q1126.92974
median127.02798
Q3127.12712
95-th percentile127.94817
Maximum129.36685
Range2.986726
Interquartile range (IQR)0.19738685

Descriptive statistics

Standard deviation0.4363145
Coefficient of variation (CV)0.0034326533
Kurtosis9.185305
Mean127.10707
Median Absolute Deviation (MAD)0.0998957
Skewness2.4185872
Sum12710.707
Variance0.19037034
MonotonicityNot monotonic
2023-12-10T18:41:42.474792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0374252 1
 
1.0%
127.014167 1
 
1.0%
127.085061 1
 
1.0%
127.0872333 1
 
1.0%
126.8367559 1
 
1.0%
126.9036032 1
 
1.0%
127.0877536 1
 
1.0%
126.9220649 1
 
1.0%
126.9156678 1
 
1.0%
127.0486233 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.3801251 1
1.0%
126.428763 1
1.0%
126.4479239 1
1.0%
126.4590822 1
1.0%
126.4846966 1
1.0%
126.6039679 1
1.0%
126.6416049 1
1.0%
126.6456903 1
1.0%
126.7077575 1
1.0%
126.7110328 1
1.0%
ValueCountFrequency (%)
129.3668511 1
1.0%
128.8941382 1
1.0%
128.2027498 1
1.0%
127.9539564 1
1.0%
127.9526665 1
1.0%
127.9479381 1
1.0%
127.9439426 1
1.0%
127.727873 1
1.0%
127.7270786 1
1.0%
127.4939527 1
1.0%

Interactions

2023-12-10T18:41:35.176410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:33.956227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:34.435716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:35.358124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:34.104142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:34.723596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:35.531315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:34.264348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:34.891635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:41:42.654892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shp_cdshp_nmshp_ctprvn_addrshp_signgu_addrshp_emd_addrshp_detail_addrshp_lashp_lo
shp_cd1.0001.0000.7840.7891.0001.0000.9570.551
shp_nm1.0001.0000.7890.9760.9870.9770.9590.000
shp_ctprvn_addr0.7840.7891.0000.9950.9970.9200.9130.955
shp_signgu_addr0.7890.9760.9951.0000.0000.7520.9690.995
shp_emd_addr1.0000.9870.9970.0001.0000.9770.9920.998
shp_detail_addr1.0000.9770.9200.7520.9771.0000.0000.971
shp_la0.9570.9590.9130.9690.9920.0001.0000.789
shp_lo0.5510.0000.9550.9950.9980.9710.7891.000
2023-12-10T18:41:42.994728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shp_ctprvn_addrshp_signgu_addr
shp_ctprvn_addr1.0000.729
shp_signgu_addr0.7291.000
2023-12-10T18:41:43.137803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shp_cdshp_lashp_loshp_ctprvn_addrshp_signgu_addr
shp_cd1.000-0.4380.2610.7750.502
shp_la-0.4381.000-0.1250.7510.615
shp_lo0.261-0.1251.0000.6500.730
shp_ctprvn_addr0.7750.7510.6501.0000.729
shp_signgu_addr0.5020.6150.7300.7291.000

Missing values

2023-12-10T18:41:35.742765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:41:35.970317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

shp_cdshp_nmshp_ctprvn_addrshp_signgu_addrshp_emd_addrshp_detail_addrshp_lashp_lo
010009씨티마트서울강남구도산대로 49길837.523351127.037425
141523권선베스토아경기수원시권선구 세권로181번길20-2337.256316127.025832
210033태광통신서울성북구동소문로248, 105동 제1층 1호(길음동, 삼부아파트)37.602698127.023674
310035인터뱅세계주류서울강남구언주로64737.514353127.03497
410039베스토아서울강남구역삼로16637.495062127.037259
510045아셈마트서울강남구영동대로112길10, 1층 108호(삼성동, 대화빌딩)37.514992127.061034
610047부흥청과서울송파구동남로31837.503697127.1387
741524송정복권명당울산북구화산6길8, 1층 106호(화봉동, S타워)35.59695129.366851
810076행복슈퍼서울성북구동소문로4937.591024127.011162
910081현대복권방서울동대문구홍릉로3길6-137.581909127.044157
shp_cdshp_nmshp_ctprvn_addrshp_signgu_addrshp_emd_addrshp_detail_addrshp_lashp_lo
9011047대일수퍼대전서구월평중로24번길4836.356522127.366507
9111067가나대전중구선화로836.324793127.410863
9211085서산터미널복권방충남서산시안견로19036.781211126.459082
9311086시민슈퍼복권방충남서산시문화로5136.785684126.447924
9411087나눔로또,토토충남서산시무학로174236.757783126.428763
951109221시수퍼충남아산시온천대로1528-136.780474127.007302
9611107복권나라충북제천시청풍호로5237.126529128.20275
9711116SUN마트 예대점충북청주시청원구 덕벌로5번길2736.65771127.493953
9811124영화마을(현대점)충북청주시청원구 주성로18436.671889127.486614
9911132피시복권방충북청주시흥덕구 1순환로513번길436.646529127.465201